Wang et al. noticed in [1]} that multi-modal networks are prone to overfitting due to their increased capacity and different modalities overfit and generalize at different rates. So, they proposed a complex Gradient-Blending training. Xiao et al. noticed also different dynamic of training depending on the modality [2]} and propose to randomly drop the audio path during the training. Unlike [2]}, when training unimodal networks, we notice that the audio network need more epoch to reach overfitting compared to the visual network. Therefore, we follow the idea of [2]} and randomly drop the upgrade of the visual path, to train more the audio path.
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